Processing data for driving automation system

ABSTRACT

A method of processing data for a driving automation system, the method comprising steps of: obtaining image data from a camera of an autonomous vehicle, AV; image processing the image data to obtain a vehicle registration mark, VRM, of another vehicle within the surrounding area of the AV; looking up the VRM in a vehicle information database to obtain information indicative of the make, the model and the date of manufacture of the other vehicle; looking up information indicative of the make, the model and the date of manufacture of the other vehicle in a vehicle dimensions database to obtain at least one dimension of the other vehicle; and updating a context of the autonomous vehicle based on said at least one dimension of the other vehicle.

FIELD OF TECHNOLOGY

The present disclosure relates to processing data for a drivingautomation system.

BACKGROUND

Autonomous vehicles, of any level of driving autonomy, rely on a rangeof sensors to assist with the autonomous drive. However, the sensorshave limitations and can be fed with a wide range of data to augment thecapabilities and even assist with predictive driving qualities, tofurther mimic driver anticipation. In one particular instance, camerasand LiDAR are used to identify the presence of other vehicles in thesurroundings of an autonomous vehicle and to give a rough prediction onwhat the type of vehicle may be, such as a motorbike, a car, a bus or aheavy goods vehicle, HGV. A visualization of the surroundings of theautonomous vehicle can then be shown on the instrument cluster/centerconsole of the vehicle to give feedback to a driver about what thevehicle “sees”.

Improvements in the information provided to a driving automation systemabout other vehicles that are present within the surrounding of anautonomous vehicle are desirable.

SUMMARY

Accordingly, there is provided a method, a computer program and acomputing device as detailed in the claims that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

Examples of the present disclosure will now be described with referenceto the attached figures, in which:

FIG. 1 is a flowchart illustrating a method in accordance with thepresent disclosure;

FIG. 2 is a flowchart illustrating steps of a method in accordance withthe present disclosure; and

FIGS. 3 and 4 are block diagrams of computing devices in accordance withthe present disclosure.

DETAILED DESCRIPTION

The following describes a method of processing data for a drivingautomation system. The method includes obtaining image data from acamera of an autonomous vehicle. The image data comprises at least oneimage of a surrounding area of the autonomous vehicle. The methodincludes image processing the image data to obtain a vehicleregistration mark, VRM, of another vehicle within the surrounding area.The method includes looking up the VRM of the other vehicle in a vehicleinformation database to obtain information indicative of the make, themodel and the date of manufacture of the other vehicle. The vehicleinformation database contains information indicative of each of a make,a model and a date of manufacture for each of a plurality of VRMs. Themethod includes looking up information indicative of the make, the modeland the date of manufacture of the other vehicle in a vehicle dimensionsdatabase to obtain at least one dimension of the other vehicle. Thevehicle dimensions database contains at least one respective dimensionfor each of a plurality of vehicles, each of the plurality of vehicleshaving a respective make, a respective model and a respective date ofmanufacture. The method includes updating a context of the autonomousvehicle based on said at least one dimension of the other vehicle.

The following describes a method for retrieving more detail about avehicle in a surrounding area of an autonomous vehicle so that anadditional data point is available to the vehicle's driving automationsystem. The additional data point may be used for building up theinformation displayed in the instrument cluster of the autonomousvehicle whilst also providing additional visibility that cameras andLiDAR on the autonomous vehicle would be otherwise unable to detect.

Levels of driving automation are defined in SAE International standardJ3016 ranging from no driving automation (level 0) to full drivingautomation (level 5). The present disclosure relates to autonomousvehicles operating at level 3 (conditional driving automation), level 4(high driving automation) or level 5, as defined in J3016.

For simplicity and clarity of illustration, reference numerals may berepeated among the figures to indicate corresponding or analogouselements. Numerous details are set forth to provide an understanding ofthe examples described herein. The examples may be practiced withoutthese details. In other instances, well-known methods, procedures, andcomponents are not described in detail to avoid obscuring the examplesdescribed. The description is not to be considered as limited to thescope of the examples described herein.

FIG. 1 is a flow diagram showing an example method 100 of processingdata for a driving automation system. The method comprises steps asfollows. The method comprises obtaining 102 image data from a camera ofan autonomous vehicle on which the driving automation system isoperating. The image data comprises at least one image of a surroundingarea of the autonomous vehicle. The method then comprises imageprocessing 104 the image data to obtain a vehicle registration mark,VRM, of another vehicle within the surrounding area of the autonomousvehicle. The method proceeds to looking up 106 the VRM of the othervehicle in a vehicle information database to obtain informationindicative of the make, the model and the date of manufacture of theother vehicle. The vehicle information database contains informationindicative of each of a make, a model and a date of manufacture for eachof a plurality of VRMs. The method then proceeds to looking up 108information indicative of the make, the model and the date ofmanufacture of the other vehicle in a vehicle dimensions database toobtain at least one dimension of the other vehicle. The vehicledimensions database contains at least one respective dimension for eachof a plurality of vehicles. Each of the plurality of vehicles isidentified by a respective make, a respective model and a respectivedate of manufacture. The method then proceeds to updating a context ofthe autonomous vehicle based on said at least one dimension of the othervehicle.

The vehicle information database may, for example in the case of the UK,comprise the driver and vehicle licensing agency, DVLA, UK VehicleDatabase. The at least one dimension of the other vehicle may be atleast one of a width, a length and a height of the other vehicle.

In an example, the step of obtaining image data from a camera of theautonomous vehicle on which the driving automation system is operatingand the step of image processing the image data to obtain a vehicleregistration mark, VRM, of another vehicle within the surrounding areaof the autonomous vehicle are performed at the autonomous vehicle. Thestep of looking up the VRM of the other vehicle in a vehicle informationdatabase is performed at a server. The method 100 comprises anadditional step of transmitting a request signal from the autonomousvehicle to the server. The request signal comprises an indication of theVRM of the other vehicle.

In an example, the step of looking up the make, the model and the dateof manufacture of the other vehicle in a vehicle dimensions database isperformed at the server. The method 100 comprises an additional step oftransmitting a response signal from the server to the autonomousvehicle. The response signal comprises an indication of the at least onedimension of the other vehicle.

In an example, the method 100 comprises an additional step oftransmitting a response signal from the server to the autonomousvehicle. The response signal comprises an indication of the make, themodel and the date of manufacture of the other vehicle obtained from thevehicle information database. The step of looking up the make, the modeland the date of manufacture of the other vehicle in a vehicle dimensionsdatabase is performed at the autonomous vehicle, following receipt ofthe response signal.

In an example, updating the context of the autonomous vehicle comprisesadding the at least one dimension of the other vehicle to the context.

In an example, updating the context of the autonomous vehicle comprisesdetermining whether a value of the at least one dimension of the othervehicle obtained from the vehicle dimensions database corresponds to avalue of the at least one dimension of the other vehicle and, based onthe determining, updating a value of the at least one dimensions of theother vehicle within the context.

In an example, the method 100 comprises an additional step of providingthe updated context to an instrument cluster of the autonomous vehicle.The updated context may alternatively or additionally be provided to atleast one sensor of the autonomous vehicle.

In an example, the step of image processing the image data to obtain thevehicle registration mark, VRM, of the other vehicle is performed usingcomputer vision, such as the OpenCV library of programming functions.

In an example, the request signal is transmitted from the autonomousvehicle using a mobile communications network. The response signal mayalso be received by the autonomous vehicle using the mobilecommunications network.

In an example, the method 100 comprises, responsive to updating thecontext, an additional step of modifying a behavior of the drivingautomation system, to modify a state of the autonomous vehicle.

In an example, the method 100 comprises an additional step of generatinga control signal comprising instructions configured to cause the drivingautomation system to set a distance between the autonomous vehicle andthe other vehicle depending on the at least one dimension of the othervehicle.

In an example, the method 100 comprises an additional step of generatinga control signal comprising instructions configured to cause the drivingautomation system to determine an overtaking procedure for theautonomous vehicle to overtake the other vehicle depending on the atleast one dimension of the other vehicle.

In this example, the at least one dimension comprises the length of theother vehicle and optionally also the width of the other vehicle.

As described above, some of the steps of the method 100 are performed atthe autonomous vehicle and some of the steps are performed at theserver. FIG. 2 is a flow diagram showing the steps of an example method200 that are performed at the autonomous vehicle. It will be appreciatedthat the other steps are performed at the server.

In this example, the method 200 starts with retrieving 202 image/visualdata from the camera of the autonomous vehicle. The method then proceedsto processing the images using computer vision, CV, for example usingthe OpenCV library of programming functionalities, and determining 206whether there is a VRM present within the images. The method 200 may,for example, perform automatic number plate recognition, ANPR, on thecamera images to obtain the VRM of another vehicle within thesurroundings of the autonomous vehicle.

The method proceeds to submitting 208 a request for vehicle informationfor the obtained VRM. A request signal is generated that contains anindication of the obtained VRM and the request signal is transmittedfrom the autonomous vehicle to a server. The request signal istransmitted on a mobile communications network from the autonomousvehicle.

For example, the request signal may take the form:

{ “vrm”: “BB19 AXB” }

The request signal is received by the server at which the step oflooking up 106 the VRM in the vehicle information database is performed,to obtain information indicative of the make, the model and the date ofmanufacture of the other vehicle.

For example, the vehicle information obtained from the vehicleinformation database for VRM “BB19 AAB” may take the form:

{ “make”: “HONDA” “model”: “CR-V” “year”: “2019” }

The step of looking up 108 the information indicative of the make, themodel and the date of manufacture of the other vehicle in a vehicledimensions database is also performed at the server, to obtain at leastone dimension of the other vehicle. In this example, a length, a widthand a height are retrieved for the other vehicle, identified by itsmake, model and date of manufacture, from the vehicle dimensionsdatabase. A response signal is then generated at the server andtransmitted to the autonomous vehicle; the response signal is receivedat the autonomous vehicle on a mobile communications network.

The response signal comprises the at least one dimension and may, forexample, take the form:

{ “height”: “2000” “width”: “1700” “length”: “4700” }

The response signal may also comprise the make, model and yearinformation obtained from the vehicle information database.

Although the request signal and the response signal are transmitted fromand received at the autonomous vehicle on a mobile communicationsnetwork it will be appreciated that a remaining part of the route fromthe autonomous vehicle to the server may comprise other communicationsnetworks.

The method, at the autonomous vehicle, includes checking 210 whethervehicle information has been received from the server. If vehicleinformation has not been received, the method ends 220, or mayalternatively loop back after a preset time to perform the check 210again until the information is received.

Responsive to receiving the vehicle information, the method proceeds toprocessing 212 the vehicle dimensions (response parameters) and updating214 the instrument cluster and feed sensors with context.

In an alternative example, the response signal may comprise anindication of the make, model and year of manufacture, and the step oflooking up the make, model and year of manufacture in the vehicledimensions database, to obtain the vehicle dimensions, is performed atthe autonomous vehicle. In this alternative, an instance of the vehicledimensions database is maintained at the autonomous vehicle.

The main issue with using visualisation and mapping technology ishandling error rates but it does require the correct positioning inorder to perform the visual and mapping checks. This means when anautonomous vehicle is behind another it can be very difficult for thedriving automation system to “see around” the vehicle in front becauseof the viewing angles. However, by using automatic number platerecognition technology, an autonomous vehicle is able to determine theexact vehicle dimensions of another vehicle in its surroundings.

Once the response signal is received, it arms the driving automationsystem on the autonomous vehicle with the ability to make additionaldecisions that it would not have had access to. The methods describedabove may enable the detection vehicles of certain dimensions from whicha following vehicle should maintain a larger distance than for astandard vehicle. For example, long vehicles require drivers of othervehicles to maintain greater distances because drivers of long vehiclesare unable to see vehicles behind them when the vehicles behind are tooclose.

The methods described above may enable the driving automation system totailor an overtaking maneuver depending on the dimensions of the vehicleto be overtaken. For example, knowing the length of the vehicle to beovertaken may enable the driving automation system to travel furtherpast a long vehicle or a tractor-trailer before pulling in than whenovertaking a car, and knowing the width of the vehicle to be overtakenmay enable the driving automation system to determine whether it is safeto overtake a wide vehicle and to modify the lane positioning of theautonomous vehicle during the overtaking maneuver.

The methods described may enable an instrument cluster of an autonomousvehicle to present a more accurate representation of what the autonomousvehicle “sees” because of the detailed information about the dimensionsof the other vehicles that the autonomous vehicle can “see”. This mayalso bring more confidence to the end user that the vehicle is assessingthe surroundings correctly. This is important as it is factors like thiswhich are relied upon to help technology adoption.

The methods described above may reduce mis-classification of objects.For example, knowing the length of a vehicle in front may prevent along-vehicle being incorrectly identified as a van or a standard lengthtractor-trailer, and vice versa. And if images of an object do notinclude a VRM, the object may be determined not to be a vehicle,preventing buildings and street furniture being mis-classified asvehicles.

Corresponding examples apply equally to the computer program andcomputing devices described below.

In an example, a computer program is provided which when executed by atleast one processor is configured to implement that steps of the abovedescribed methods.

Steps of the above described methods may be implemented by a computingdevice, which may form part of a driving automation system of anautonomous vehicle. A block diagram of one example of a computing device300 is shown in FIG. 3. The computing device 100 comprises processingcircuitry 310 and interface circuitry 312.

The processing circuitry is configured to obtain image data from acamera of an autonomous vehicle. The image data comprises at least oneimage of a surrounding area of the autonomous vehicle. The processingcircuitry is configured to image process the image data to obtain avehicle registration mark, VRM, of another vehicle within thesurrounding area.

The processing circuitry is configured to cause a request signal to betransmitted to a server. The request signal comprises an indication ofthe VRM of the other vehicle and is configured to cause the VRM of theother vehicle to be looked up in a vehicle information database toobtain information indicative of the make, the model and the date ofmanufacture of the other vehicle. The vehicle information databasecontains information indicative of each of a make, a model and a date ofmanufacture for each of a plurality of VRMs. The processing circuitry isconfigured to obtain at least one dimension of the other vehicle and toupdate a context of the autonomous vehicle based on the obtained atleast one dimension of the other vehicle.

In an example, the processing circuitry is configured to receive aresponse signal from the server, the response signal comprising anindication of the at least one dimension of the other vehicle. Theprocessing circuitry thereby obtains the at least one dimension of theother vehicle within the response signal from the server.

In an example, the processing circuitry is configured to receive aresponse signal from the server, the response signal comprisinginformation indicative of a make, a model and a date of manufacture ofthe other vehicle. The processing circuitry is configured to look up theinformation indicative of the make, the model and the date ofmanufacture of the other vehicle in a vehicle dimensions database toobtain the at least one dimension of the other vehicle. The vehicledimensions database contains at least one respective dimension for eachof a plurality of vehicles, each of the plurality of vehicles having arespective make, a respective model and a respective date ofmanufacture.

A block diagram of another example of a computing device 400 is shown inFIG. 4. The computing device 400 comprises processing circuitry 310, asdescribed above, interface circuitry in the form of a communicationsubsystem 412, and memory 414.

In this example, communication functions are performed through thecommunication subsystem 104. The communication subsystem 104 receivesresponse messages from and sends request messages to a wireless network(not shown), to which the server is connected. The wireless network 150may be any type of wireless network, including, but not limited to, datawireless networks, voice wireless networks, and networks that supportboth voice and data communications.

The processing circuitry 310 interacts with the communication subsystemand other components, such as the memory 414 and a camera of theautonomous vehicle. The memory store software programs 420 and a datastore 422, which may include the vehicle dimensions database.

The scope of the claims should not be limited by the preferred examplesset forth above but should be given the broadest interpretationconsistent with the description as a whole.

What is claimed is:
 1. A method of processing data for a drivingautomation system, the method comprising steps of: obtaining image datafrom a camera of an autonomous vehicle, the image data comprising atleast one image of a surrounding area of the autonomous vehicle; imageprocessing the image data to obtain a vehicle registration mark, VRM, ofanother vehicle within the surrounding area; looking up the VRM of theother vehicle in a vehicle information database to obtain informationindicative of the make, the model and the date of manufacture of theother vehicle, the vehicle information database containing informationindicative of each of a make, a model and a date of manufacture for eachof a plurality of VRMs; looking up information indicative of the make,the model and the date of manufacture of the other vehicle in a vehicledimensions database to obtain at least one dimension of the othervehicle, the vehicle dimensions database containing at least onerespective dimension for each of a plurality of vehicles, each of theplurality of vehicles having a respective make, a respective model and arespective date of manufacture; and updating a context of the autonomousvehicle based on said at least one dimension of the other vehicle. 2.The method of claim 1, wherein obtaining image data and image processingthe image data are performed at the autonomous vehicle and looking upthe VRM of the other vehicle in a vehicle information database isperformed at a server, and further comprising transmitting a requestsignal from the autonomous vehicle to the server, the request signalcomprising an indication of the VRM of the other vehicle.
 3. The methodof claim 2, wherein looking up the make, the model and the date ofmanufacture of the other vehicle in a vehicle dimensions database isperformed at the server, and further comprising transmitting a responsesignal from the server to the autonomous vehicle, the response signalcomprising an indication of the at least one dimension of the othervehicle.
 4. The method of claim 2, further comprising transmitting aresponse signal from the server to the autonomous vehicle, the responsesignal comprising an indication of the make, the model and the date ofmanufacture of the other vehicle, and wherein looking up the make, themodel and the date of manufacture of the other vehicle in a vehicledimensions database is performed at the autonomous vehicle.
 5. Themethod of claim 1, wherein updating the context of the autonomousvehicle comprises adding the at least one dimension of the other vehicleto the context.
 6. The method of claim 1, wherein updating the contextof the autonomous vehicle comprises determining whether a value of theat least one dimension of the other vehicle obtained from the vehicledimensions database corresponds to a value of the at least one dimensionof the other vehicle, and based on the determining updating a value ofthe at least one dimensions of the other vehicle within the context. 7.The method of claim 1, further comprising providing the updated contextto at least one of an instrument cluster of the autonomous vehicle andat least one sensor of the autonomous vehicle.
 8. The method of claim 2,wherein the request signal is transmitted from the autonomous vehicleusing a mobile communications network.
 9. The method of claim 1, furthercomprising responsive to updating the context, modifying a drivingbehaviour of the driving automation system.
 10. The method of claim 1,further comprising generating a control signal comprising instructionsconfigured to cause the driving automation system to set a distancebetween the autonomous vehicle and the other vehicle depending on the atleast one dimension of the other vehicle.
 11. The method of claim 1,further comprising generating a control signal comprising instructionsconfigured to cause the driving automation system to determine anovertaking procedure for the autonomous vehicle to overtake the othervehicle depending on the at least one dimension of the other vehicle.12. A non-transitory computer program product which, when executed by atleast one processor of a computing device, cause the computing deviceto: obtain image data from a camera of an autonomous vehicle, the imagedata comprising at least one image of a surrounding area of theautonomous vehicle; image process the image data to obtain a vehicleregistration mark, VRM, of another vehicle within the surrounding area;look up the VRM of the other vehicle up in a vehicle informationdatabase to obtain information indicative of the make, the model and thedate of manufacture of the other vehicle, the vehicle informationdatabase containing information indicative of each of a make, a modeland a date of manufacture for each of a plurality of VRMs; look up theinformation indicative of the make, the model and the date ofmanufacture of the other vehicle in a vehicle dimensions database toobtain at least one dimension of the other vehicle, the vehicledimensions database containing at least one respective dimension foreach of a plurality of vehicles, each of the plurality of vehicleshaving a respective make, a respective model and a respective date ofmanufacture; and update a context of the autonomous vehicle based onsaid at least one dimension of the other vehicle.
 13. A computing devicecomprising: interface circuitry; and processing circuitry configured to:obtain image data from a camera of an autonomous vehicle, the image datacomprising at least one image of a surrounding area of the autonomousvehicle; image process the image data to obtain a vehicle registrationmark, VRM, of another vehicle within the surrounding area; cause arequest signal to be transmitted to a server, the request signalcomprising an indication of the VRM of the other vehicle and the requestsignal configured to cause the VRM of the other vehicle to be looked upin a vehicle information database to obtain information indicative ofthe make, the model and the date of manufacture of the other vehicle,the vehicle information database containing information indicative ofeach of a make, a model and a date of manufacture for each of aplurality of VRMs; obtain at least one dimension of the other vehicle;and update a context of the autonomous vehicle based on said at leastone dimension of the other vehicle.
 14. The computing device of claim13, wherein the processing circuitry is configured to receive a responsesignal from the server to obtain said at least one dimension of theother vehicle, the response signal comprising an indication of the atleast one dimension of the other vehicle.
 15. The computing device ofclaim 13, wherein the processing circuitry is configured to: receive aresponse signal from the server, the response signal comprisinginformation indicative of a make, a model and a date of manufacture ofthe other vehicle; and look up the information indicative of the make,the model and the date of manufacture of the other vehicle in a vehicledimensions database to obtain the at least one dimension of the othervehicle, the vehicle dimensions database containing at least onerespective dimension for each of a plurality of vehicles, each of theplurality of vehicles having a respective make, a respective model and arespective date of manufacture.
 16. The computing device of claim 13,wherein the processing circuitry is configured to update the context ofthe autonomous vehicle by adding the at least one dimension of the othervehicle to the context.
 17. The computing device of claim 13, whereinthe processing circuitry is further configured to provide the updatedcontext to at least one of an instrument cluster of the autonomousvehicle and at least one sensor of the autonomous vehicle.
 18. Thecomputing device of claim 13, wherein the request signal is transmittedusing a mobile communications network.
 19. The computing device of claim13, wherein the processing circuitry is further configured to,responsive to updating the context, modify a driving behaviour of thedriving automation system.
 20. The computing device of claim 13, whereinthe processing circuitry is further configured to generate a controlsignal comprising instructions configured to cause the drivingautomation system to set a distance between the autonomous vehicle andthe other vehicle depending on the at least one dimension of the othervehicle.
 21. The computing device of claim 13, wherein the processingcircuitry is further configured to generate a control signal comprisinginstructions configured to cause the driving automation system todetermine an overtaking procedure for the autonomous vehicle to overtakethe other vehicle depending on the at least one dimension of the othervehicle.